Web scraping: definition, how it works, tools, and legal framework

What is scraping?

Definition of scraping

Scraping, orweb scraping when it involves internet pages, refers to the use of a program or software to automatically retrieve specific information found on a website.

The principle is relatively simple: instead of a person visiting a page, identifying the data they are interested in, and manually copying it into a file, a scraper performs these operations automatically.

For example, it can successively open thousands of company profiles and retrieve:

• the company name;
• its industry;
• its address;
• its website;
• certain professional contact details;
• information visible on its products or services.

The results can then be stored in a spreadsheet, a B2B database or sent directly to a CRM.

Web scraping should therefore not be confused with simply copying a webpage. Its purpose is generally totransform scattered, unstructured information into actionable data on a large scale.

Good to know

Information that is publicly visible on the Internet is not automatically free to reuse. The CNIL notably reminds users that personal data that is publicly accessible remains personal data and that its reuse must comply with the GDPR.

What is the purpose of scraping?

Scraping primarily addresses the issue of volume.

Manually collecting pricing from 20 competitors might be feasible. Repeating this process every day for 10,000 product references is not. This is precisely where automation becomes valuable.

A company can use scraping to:

• track competitor pricing trends;
• identify new companies that meet its prospecting criteria;
• populate a database;
• monitor listings or job offers;
• collect reviews or public information;
• analyze search engine results;
• detect changes on specific pages;
• conduct market research;
• enrich data already present in a CRM.

In B2B, scraping can be used specifically at the start of a B2B lead generationprocess. The goal here is not necessarily to contact everyone identified immediately, but to have information available to better target the companies or profiles to be analyzed.

What types of data can be collected?

Technically, a scraper can retrieve many elements that are visible or present in a page's code:

• text;
• titles and descriptions;
• tables;
• links;
• prices;
• product references;
• categories;
• ratings and reviews;
• dates;
• business-related information;
• HTML attributes;
• certain metadata;
• structured data embedded in the source code.

Images or documents can also be identified and their links retrieved, although their subsequent download or reuse raises other issues, particularly regarding copyright.

An essential distinction must also be made betweendata concerning a businessanddata that identifies a natural person.

A SIREN number or a company's business sector does not have the same legal status as a name, a personal email address, or a personal phone number.

This distinction must be integrated from the very design phase of a scraping project.

How does scraping work?

The steps of a scraping process

A scraping project generally follows five main steps.

1. Identify the sources

You must start by precisely determining which websites or pages contain the information you are looking for.

2. Identify the data to extract

The scraper must know which elements to retrieve: company name, price, category, URL, date, description, etc.

3. Download or display the pages

The program sends requests to the targeted pages or uses an automated browser when part of the content is dynamically generated.

4. Analyze the content

The scraper identifies the elements corresponding to the defined rules: HTML tags, CSS classes, attributes, JSON structures, or other elements present on the page.

5. Store and clean the data

The information obtained is saved to a file or database, then checked, normalized, and potentially deduplicated.

In a professional environment, a sixth step quickly becomes essential:automating execution and updates.

How a scraper retrieves data from a web page

A web page is built using several technologies, primarily HTML, CSS, and JavaScript.

HTML contains a large part of the page structure. Let's look at a simplified example:

<span class="prix">49 €</span>

A scraper can be instructed to "retrieve the content of all elements with the class prix".

The result obtained here will be: 49 €.

On a page containing 100 products built in a similar way, the same rule allows you to automatically retrieve all 100 prices.

In reality, modern websites are obviously more complex. Information can be:

• loaded only after the page is opened;
• retrieved via a JavaScript request;
• displayed after a click;
• spread across multiple pages;
• accessible only after authentication;
• loaded while scrolling.

The choice of technology used for scraping therefore depends directly on how the site displays its data.

Main extracted data formats (CSV, Excel, JSON, XML)

Once collected, information must be stored in a format suited to its intended use.

CSV

CSV is particularly practical for simple tabular data. It can easily be opened in Excel, Google Sheets, or imported into many software programs.

Excel

The Excel format is suitable when users want to directly view, filter, or rework data in a spreadsheet.

JSON

JSON is widely used for transmitting data between applications. It allows for the representation of more complex structures and integrates particularly well with APIs and web applications.

XML

XML is also a structured data format. It remains in use across many professional systems, even though JSON is now very common in modern web applications.

The right format therefore depends on how the data will be processed after extraction. For one-off analysis, CSV or Excel are often sufficient. To automatically feed an application or platform, JSON will generally be more suitable.

Different types of scraping

Not all scraping projects rely on the same method. The technique depends primarily on the source, the volume of data, and the complexity of the site.

Web scraping

Web scraping consists of directly retrieving information found on web pages.

This is the usage generally referred to when simply speaking of "scraping."

A web scraper can, for example, browse an online catalog, open each product page, and retrieve:

• the title;
• the price;
• the availability;
• the SKU;
• the category.

It is particularly well-suited for information organized in a relatively repetitive structure.

Data scraping

The term data scraping has a broader meaning.

It describes the automated extraction of data from a digital source, which is not necessarily a standard web page.

This may involve extracting data from:

• documents;
• files;
• applications;
• feeds;
• digital interfaces.

Web scraping is therefore a specific form of data scraping.

Screen scraping

Screen scraping is more about retrieving information exactly as it appears on the screen.

This technique is particularly useful when data is difficult to access in a directly usable format within the code, or when working with certain legacy interfaces.

It is generally less robust than extraction based directly on the data structure. A visual change to the interface can be enough to disrupt the scraper's operation.

API scraping

Some web applications communicate with their servers via APIs.

In this case, it can sometimes be more efficient to retrieve information directly from an interface designed to transmit structured data, provided its use is authorized.

A JSON API response is generally much easier to process than the HTML of an entire page.

However, be careful: discovering that a site uses an API in the background does not automatically mean that this interface can be used freely. You must check the applicable terms of access and use.

Static and dynamic (JavaScript) scraping

The difference between static and dynamic scraping is fundamental.

Astatic site, in this context, returns the requested information directly in the HTML received by the scraper. A simple HTTP request followed by HTML parsing may therefore be sufficient.

On adynamicsite, some data only appears after JavaScript execution.

In this case, a tool capable of controlling a browser may become necessary. It loads the page just as a user would, allows the scripts to run, and then retrieves the information once it is displayed.

Here is a summary of the main approaches:

Good to know

Before using an automated browser, check whether the information you need is already present in the HTML or available through an authorized API. A simpler architecture will generally be faster, more stable, and less expensive to maintain.

Main use cases for scraping

B2B lead generation

Scraping can make it easier to identify companies that meet specific criteria:

• industry;
• location;
• size;
• technology used;
• presence in certain directories;
• specific activity.

This information can then serve as a starting point for sales qualification.

The challenge, however, is to avoid a purely quantitative approach. Accumulating thousands of contacts without a targeting strategy rarely yields good results.

A structured approach must link data collection, qualification, and B2B prospectingstrategy.

A B2B lead generation agency can help guide this process by focusing on targeting, qualification, and appointment setting rather than just accumulating data.

Competitive intelligence

Web scraping allows you to automate part of your competitive intelligence efforts.

A company can regularly monitor:

• the emergence of new offers;
• price changes;
• the launch of new features;
• content publication;
• catalog updates;
• specific public information regarding competitors.

The main advantage lies in the frequency of data collection. Instead of conducting occasional competitive studies, a company can build a continuous monitoring system.

Price monitoring

Price tracking is one of the classic use cases for scraping.

In e-commerce, it can be used to regularly compare prices across different platforms.

The collected information can then feed into:

• a dashboard;
• alerts;
• positioning analysis;
• a pricing system.

However, you must take into account the applicable laws and the contractual terms of the sites in question. Some platforms explicitly regulate or prohibit automated extraction tools.

Customer review collection

Public reviews can be useful for identifying consumer expectations, objections, or reasons for dissatisfaction.

A structured analysis can, for example, highlight:

• the most appreciated features;
• frequently reported issues;
• recurring expectations;
• the vocabulary used by customers;
• the competitive advantages of rivals.

Collecting reviews must, however, take into account the rights associated with the content and, where applicable, any personal data appearing in the contributions.

Lead generation

Scraping can help identify organizations that match an ideal customer profile.

It is notably possible to look for signals such as:

• the company's business activity;
• its location;
• specific hiring;
• the use of particular technologies;
• its expansion into new markets;
• certain publicly visible changes.

The goal is to obtain contextual data to help prioritize prospecting efforts.

Collecting email addresses, however, is a separate challenge. To learn more about this, check out our guide on methods for finding business email addresses.

Database enrichment

Existing databases gradually become obsolete due to companies changing addresses, new hires, role changes, or missing information.

Scraping can be part of a data enrichment process by automatically searching for specific additional information.

A verification step remains essential, however. Information available online can be outdated, contradictory, or incorrect.

SEO analysis

Scraping is also widely used in SEO tools.

It is particularly useful for retrieving:

• title tags;
• meta descriptions;
• Hn headings;
• internal links;
• HTTP status codes;
• canonical tags;
• information present on pages;
• internal linking data.

SEO crawlers use this exact logic to navigate a site and analyze its structure.

Is scraping legal?

There is no universal answer to this question.

Scraping is not automatically prohibited, but the mere fact that information is accessible via a browser does not mean it can be copied, stored, and reused without any restrictions.

The legality of a project depends in particular on:

• the data collected;
• the potential existence of personal data;
• the purpose of the processing;
• the rights attached to the content or databases;
• the site's terms of use;
• the technical methods used;
• the quantity of information extracted;
• how the data will subsequently be used.

What the regulations say

Several sets of rules may apply simultaneously.

When a database represents a substantial investment, European law grants the database maker the right to prohibit the extraction or reuse of all or a substantial part of its content. The European Database Directive also targets certain repeated and systematic extractions of insubstantial parts when they conflict with the normal exploitation of the database.

Copyright may also apply when the collected elements constitute protected works: texts, photographs, graphic designs, or other original content.

Finally, personal data is subject to GDPR rules.

You must therefore evaluate each project on a case-by-case basis rather than looking for a general rule stating that "all scraping is authorized" or "all scraping is prohibited."

Scraping and GDPR

GDPR becomes relevant as soon as the information collected allows for the direct or indirect identification of a natural person.

This may include:

• a first and last name;
• a personal email address;
• a personal or professional phone number linked to an individual;
• an individual profile;
• certain professional information associated with a person.

The CNIL explicitly states that personal data publicly accessible on the Internetremains personal data. Its public nature does not, therefore, allow it to be freely reused for any purpose.

A data controller must, among other things, determine a legal basis, respect the principles of purpose limitation and data minimization, inform individuals when required, and allow them to exercise their rights.

Furthermore, when scraping publicly accessible data, the CNIL recommends limiting collection to necessary data and defining precise criteria in advance to reduce the amount of information collected.

To learn more about this issue in a commercial context, check out our content dedicated to the rules of commercial prospecting and the CNIL.

Good to know

The GDPR data minimization principle means, in practical terms, that a scraper should not collect “everything available just in case.” You need to determine in advance which information is genuinely necessary for the intended purpose.

Website terms of use

General terms of use are another element to check before scraping a site.

Some platforms explicitly prohibit:

• bots;
• unauthorized crawlers;
• scraping;
• automated extraction;
• bypassing access restrictions.

LinkedIn, for example, currently states in its User Agreement that it is prohibited to develop or use software, scripts, robots, crawlers, or technologies intended to scrape or copy its services, including profiles and other platform data.

Google’s terms also govern automated access to content and specifically prohibit the use of automated means when it violates machine-readable page instructions, such as certain robots.txt directives.

Certain Amazon terms of service also explicitly prohibit the use of data mining, robots, or similar data gathering and extraction tools without authorization.

Before launching a scraper on a third-party platform, reading its terms of service is not just a formality: it is a project milestone.

Legal risks

A poorly managed project can expose a company to several risks:

• liability regarding data protection;
• breach of service terms;
• infringement of database rights;
• copyright infringement;
• account suspension or termination;
• technical restrictions;
• cease and desist requests regarding data collection;
• litigation with the platform operator.

The level of risk increases significantly when extraction involves large volumes, relies on bypassing security measures, or concerns sensitive or irrelevant personal information.

Best practices for staying compliant

Before starting any scraping project, create a checklist.

Start by precisely identifying the source and the purpose of the collection.

Then check:

1. the website's terms of use;
2. any instructions intended for robots;
3. whether the data is personal or not;
4. the legal basis when GDPR applies;
5. the rights associated with the content or the database;
6. the amount actually required;
7. the procedures for informing individuals and exercising their rights;
8. the retention period;
9. the security measures applied to the data.

For high-stakes projects, getting legal approval beforehand is much easier than trying to fix non-compliant collection after millions of data points have already been stored.

Which tools should you use for scraping?

There is no single best scraping tool.

The choice mainly depends on the user's technical skill level, the complexity of the pages, the volume of extraction, and the desired level of automation.

To help you choose, here is a preliminary summary:

No-code tools

No-code tools allow you to build a scraper without having to develop a program from scratch.

They are particularly useful for:

• ad-hoc needs;
• marketing teams;
• quick tests;
• projects that do not require complex architecture.

Octoparse

Octoparse offers a visual approach to web scraping, allowing you to select the desired data and create workflows without writing the entire scraper code from scratch.

The platform highlights its no-code scraper creation and its ability to extract data from dynamic websites.

Octoparse can be useful when you want to quickly:

• extract lists;
• manage pagination;
• organize data;
• automate recurring tasks.

Its simplicity does not, of course, exempt you from verifying that the intended collection is authorized.

ParseHub

ParseHub also operates via a visual interface.

Users can select the information they wish to retrieve directly on a page. The platform notably supports interactions with pages using AJAX, forms, or dropdown menus, and allows for exports to JSON or Excel.

It may be suitable for users looking to scrape interactive pages without developing a full-scale crawler.

Web Scraper

Web Scraper is best known for its browser extension that allows you to build extraction sitemaps.

A cloud offering then allows you to add features such as:

• scheduling ;
• API ;
• automated execution ;
• export ;
• task tracking.

The official documentation highlights these features in its Web Scraper Cloud offering.

Developer libraries

As soon as a project becomes specific, large-scale, or deeply integrated into a company's information system, programmatic solutions provide greater control.

Beautiful Soup

Beautiful Soup is a Python library for extracting information from HTML and XML documents.

It allows you to navigate and query a document tree to easily find the tags or attributes you are looking for.

It is particularly well-suited for:

• parsing an HTML page ;
• selecting elements ;
• extracting text and attributes ;
• cleaning up certain structures.

However, Beautiful Soup is not a browser. Used on its own, it is therefore not the most suitable solution when data requires complex JavaScript execution before it can appear.

Scrapy

Scrapy is a Python framework specifically designed for crawling and web scraping.

Its documentation describes it as a high-level framework for crawling websites and extracting structured data.

It becomes particularly useful when the project needs to handle:

• a large number of pages;
• multiple URL types;
• crawling rules;
• processing pipelines;
• large-scale, recurring extractions.

It has a steeper learning curve than a no-code solution, but it allows for the construction of much more customized architectures.

Selenium

Selenium allows you to automate a real web browser.

The program can therefore replicate interactions such as:

• opening a page;
• clicking;
• entering text;
• selecting an element;
• waiting for content to appear.

The Selenium project states that WebDriver drives browsers natively and enables the automation of major browsing technologies.

This approach is useful when content is heavily dependent on interactions performed within the page.

However, it is generally more resource-intensive than a simple HTTP request for static pages.

Playwright

Playwright is also a browser automation solution.

It allows you to control Chromium, Firefox, and WebKit, among others.

For scraping projects involving modern web applications, it can be used to:

• load a dynamic page;
• wait for specific elements;
• perform actions;
• retrieve content generated after JavaScript execution.

Like Selenium, however, it should only be used when this level of complexity is truly necessary.

Online scraping platforms

Beyond locally installed tools, there are now many platforms that offer a complete data collection infrastructure.

These services can handle part of the:

• scraper execution;
• scheduling;
• storage;
• monitoring;
• export;
• API integration.

Their main advantage is operational: the company avoids having to maintain the entire technical infrastructure itself.

In return, costs can rise quickly with the volume and frequency of execution.

For use cases directly focused on sales prospecting, it may also be relevant to compare scraping withB2B prospecting softwarealready having data or features suited to the need.

The limitations and challenges of scraping

A scraper that works perfectly when first created will not necessarily stay that way.

The web is constantly evolving: modified HTML structures, URL changes, new authentication methods, changes in pagination, or the emergence of new security measures.

Anti-scraping protections

Websites can implement various mechanisms designed to detect or limit automated access.

For example:

• rate limiting;
• monitoring browsing behavior;
• authentication;
• access restrictions;
• automated traffic detection;
• temporary blocking.

The goal of a professional project should not be to engage in a constant race to bypass these protections.

When a site clearly demonstrates its intent to limit automated access, the best solution is to look for an API, a license, a partnership, or another data source.

CAPTCHAs

CAPTCHAs are primarily used to distinguish human users from automated systems.

When a scraper frequently triggers CAPTCHAs, it is usually a signal that the site does not want this type of automated browsing.

Systematically trying to bypass this mechanism increases both the technical complexity and the risks associated with the project.

IP address limitations

A server may also limit the number of requests from a single IP address.

A sudden spike in traffic can be considered abusive, even when pages are publicly accessible.

The best approach is therefore to:

• slow down the collection rate;
• space out requests;
• cache information that has already been retrieved;
• avoid reloading the same pages unnecessarily;
• use an API when one is available;
• request authorization when the volume becomes significant.

Sites using JavaScript

Modern web applications often display some information only after JavaScript has executed.

In this case, a simple HTML request may return an almost empty page.

Two solutions can then be considered:

• identify an authorized structured source to obtain the data;
• use an automated browser like Selenium or Playwright.

However, the second option consumes more resources and generally requires more maintenance.

The quality of collected data

Scraping never guarantees that the data obtained is accurate.

A web page may contain:

• outdated information;
• poorly formatted values;
• duplicates;
• incomplete data;
• inconsistent information.

A perfectly functional scraper can still produce a low-quality database.

This is why validation and cleaning must be considered an integral part of the project, rather than an optional task performed after the fact.

How to succeed with a scraping project?

Define the data to be collected

A common mistake is to start with the tool.

The right question is not: "Which scraper should we use?"

The first question should be:"What data do we actually need?"

Clearly define:

• the source;
• the fields;
• the frequency;
• the volume;
• the intended use;
• the retention period.

This step significantly reduces project complexity and also helps ensure better compliance with the principle of data minimization when personal data is involved.

Respect server limits

A scraper should avoid placing unnecessary load on the target site.

Reasonable data collection relies on:

• controlled frequency;
• delays between requests when necessary;
• avoiding repeated downloads of the same data;
• respecting stated limitations;
• using an appropriate API when available.

Slower but reliable and compliant extraction is generally better than maximum volume achieved for a few hours before a complete block.

Clean and structure data

Raw data is rarely ready for immediate use.

You often need to:

• normalize names;
• remove unnecessary spaces;
• harmonize dates;
• convert currencies;
• standardize phone numbers;
• remove duplicates;
• identify missing data.

An extraction project must therefore include a proper pipeline:

collection → validation → cleaning → enrichment → storage → utilization.

Good to know

In a data project, the difficulty often lies less in extraction itself than in normalization. Two pages can display exactly the same information in completely different formats.

Automating extractions

A manually executed scraper may suffice for a one-off study.

However, for daily monitoring or regular database updates, you need to plan for automated execution.

Automation can include:

• daily or weekly triggering;
• error monitoring;
• alerts;
• duplicate detection;
• automatic import into a database or CRM.

This logic aligns more broadly with the challenges of automated prospecting when data collection feeds into a sales process.

Updating data regularly

Data is only valuable if it remains fresh enough for its intended use.

The ideal frequency depends on the context.

E-commerce pricing may require several checks per day. Corporate information about a company can be updated much less frequently.

The challenge is therefore to define a frequency adapted to the actual rate at which information changes.

Increasing extractions unnecessarily raises costs, server load, and the volume of data to process without necessarily creating more value.

Scraping, crawling, and APIs: what are the differences?

These concepts are similar, but they do not refer to exactly the same operation.

Scraping vs. Crawling

Crawling primarily consists ofbrowsing pages and discovering URLs.

Scraping primarily consists ofextracting specific data from within those pages.

For example, a crawler can start from a homepage, follow all internal links, and discover 30,000 URLs.

A scraper can then open some of those pages and retrieve their titles, prices, or any other desired data.

The two techniques are therefore frequently used together.

Scraping vs. Data Mining

Scraping is about collection.

Data mining is more aboutanalyzing data to identify trends, relationships, or patterns.

Let's take an example:

• scraping 100,000 customer reviews = collection;
• analyzing these reviews to detect the main reasons for dissatisfaction = data mining or data analysis.

Scraping therefore often produces the raw material that will subsequently be analyzed.

Scraping vs. API

An API is an organized interface that allows two computer systems to communicate.

When you query an API, you generally receive a structured response directly.

With scraping, you often have to:

1. download a page;
2. identify the information within its structure;
3. extract it;
4. transform it.

An API is generally more stable when the provider officially makes it available for the intended purpose.

Here are the main differences:

When should you choose an API over scraping?

When an official API provides exactly the data you need under terms compatible with your project, it is usually the first choice.

It often provides:

• a stable data structure;
• documentation;
• clear usage rules;
• known quotas;
• less dependence on the site's layout.

Scraping becomes particularly relevant when no suitable interface exists and the intended collection remains legally and contractually permissible.

Common mistakes to avoid

Scraping without checking terms of service

Launching a tool immediately just because a page is accessible via a browser is a bad approach.

Before any automation, check:

• the terms of service;
• any developer policies;
• automated access rules;
• instructions for crawlers.

This verification is especially essential on large platforms.

Collecting personal data without a legal basis

The fact that someone has published certain information on the Internet does not make it usable without restriction.

The CNIL reminds us that the collection of publicly accessible data online remains subject to the GDPR when it involves personal data.

Therefore, define the following before collection:

• the purpose;
• the legal basis;
• the categories of data actually required;
• the notification procedures;
• the rights of the individuals;
• the retention period.

Sending too many requests

A poorly configured scraper can send hundreds or thousands of requests in a matter of seconds.

This can:

• slow down the site;
• trigger restrictions;
• lead to a block;
• make results less reliable.

It is better to adjust the frequency to actual needs and eliminate unnecessary requests.

Neglecting data cleaning

A technically successful extraction can produce:

Paris

PARIS

Paris

75000 Paris

To a human, these values seem similar. To a computer system, they can become four different entries.

A standardization step must therefore be planned before importing into the CRM or production database.

Using obsolete data

Information collected two years ago is not necessarily still accurate.

This issue is particularly important in prospecting:

• employees who have changed companies;
• companies that have moved;
• expired domains;
• activities that have evolved.

Keep the collection or verification date when the freshness of the information has operational value.

Scraping FAQ

What is scraping?

Scraping is a technique used to automatically extract information from web pages or other digital sources.

A program identifies the desired elements, collects them, and typically transforms them into structured data within a CSV, Excel, or JSON file, or a database.

Is scraping legal?

Scraping is neither systematically authorized nor systematically prohibited.

Its legality depends in particular on:

• the data involved;
• the site's terms of use;
• GDPR compliance when personal data is involved;
• content rights;
• database rights;
• the intended use of the information.

Before starting a major project, a legal analysis tailored to the specific case is therefore recommended.

What is the difference between scraping and crawling?

Crawling essentially consists of browsing pages and discovering new URLs.

Scraping consists of extracting the data present within those pages.

A crawler can therefore identify 10,000 pages on a site, while a scraper will subsequently retrieve specific information from those pages.

What are the best web scraping tools?

The best tool depends on the project.

For non-technical users, Octoparse, ParseHub, or Web Scraper allow you to create extractions with relatively little code.

For developers, Beautiful Soup is well-suited for HTML parsing, while Scrapy is better for building more comprehensive crawlers. Selenium and Playwright are particularly useful when you need to automate a browser and handle pages that rely heavily on JavaScript.

Can you scrape LinkedIn?

Technically, software can attempt to extract information displayed on LinkedIn. This does not mean that the practice is authorized by the platform.

LinkedIn’s User Agreement currently prohibits the use of software, scripts, robots, crawlers, or technologies intended to scrape or copy the services, including profiles and other data. It also prohibits certain unauthorized automated means of accessing its services.

You must also take the GDPR into account when an extraction involves personal data.

For professional use, it is therefore better to prioritize the features and interfaces officially authorized by the platform.

Can you scrape Google?

The same caution applies to Google.

Google’s terms specifically prohibit the use of automated access methods when they do not comply with the machine-readable instructions indicated on its pages.

Mass automated collection of search results should therefore not be considered freely authorized by default.

When an official interface or an authorized solution can meet your needs, it is generally a more reliable alternative.

How do you automate scraping?

To automate a scraper, you must first create a stable extraction process, then schedule its execution at regular intervals.

A workflow can, for example:

1. start every night;
2. collect new data;
3. verify its validity;
4. remove duplicates ;
5. save results ;
6. update a database ;
7. send an alert in case of an error.

The system must also account for changes in page structure, as a scraper can stop working after a simple modification to the target site.

When should you use an API instead of scraping?

An official API should generally be preferred when it provides the necessary information under conditions compatible with the project.

It often offers a more stable structure, documentation, and clearly defined terms of use.

Scraping remains highly relevant when no suitable API exists, provided you first verify the site's access terms, applicable rights, and, where personal data is involved, the obligations imposed by the GDPR.